Enterprise AI purchases stumble between demo promises and real‑world performance

Enterprises that acquire AI agent platforms frequently discover that the capabilities shown in vendor demos do not translate to production use. Demos are run on curated, clean data with expert operators, and the hardware and query types are optimised for best‑case results. In real deployments, organisations face unstructured, outdated documents, non‑expert users, and environments that lack the sandboxing and observability built into the demo, leading to lower accuracy, increased risk, and unexpected costs.

A buyer‑focused checklist has emerged to bridge this gap. It stresses verifying where an agent’s knowledge is stored, ensuring file‑grounded memory rather than opaque chat history, demanding isolated sandboxes and audit trails for any code‑execution capabilities, and scrutinising pricing models that may rise sharply at renewal. It also recommends seeking non‑reference customers to obtain candid feedback, because vendor‑provided references tend to showcase only successful cases. Applying these checks before contract signing can help organisations avoid the common procurement pitfalls that have become a structural feature of the enterprise AI market.